nits

src/axolotl/integrations/diffusion/README.md

@@ -53,8 +53,9 @@ model_type: llama
 # Standard Axolotl configuration
 datasets:
   - path: your_dataset
-    type: completion  # or conversation
+    ...
 
+# Other config
 sequence_len: 1024
 micro_batch_size: 8
 gradient_accumulation_steps: 4
@@ -85,31 +86,16 @@ The plugin uses native 4D attention masks to:
 
 Loss is computed only on masked tokens with (optional) importance weighting:
 
-```
+```python
 loss = sum(cross_entropy(pred, target) / p_mask) / total_tokens
 ```
 
-## Performance Tips
-
-### Memory Optimization
-- Bidirectional attention uses more memory than causal attention
-- Consider reducing `micro_batch_size` if you encounter OOM errors
-- Consider using gradient checkpointing, torch.compile,
-
-### Training Stability
-- Start with `noise_schedule: linear` for more predictable behavior
-- Enable `importance_weighting: true` for better gradient scaling
-
-### Convergence
-- Monitor the `diffusion_loss` and `diffusion_accuracy` metrics
-- Expect different loss curves compared to standard language modeling
-
 ## Sample Generation
 
 When `generate_samples: true`, the plugin generates samples during training:
 
 ```
-📝 Sample 1:
+Sample 1:
    Original (45 tokens): The quick brown fox jumps over the lazy dog...
    Masked (18/45 tokens, 40.0%): The [MASK] [MASK] fox [MASK] over [MASK] lazy [MASK]...
    Generated: The quick brown fox jumps over the lazy dog...
@@ -136,4 +122,4 @@ The plugin adds several metrics to track diffusion training:
 ## References
 
 - [LLaDA Paper](https://arxiv.org/abs/2404.10406)
-- [Axolotl Documentation](https://github.com/OpenAccess-AI-Collective/axolotl)
+- [Axolotl Documentation](https://docs.axolotl.ai/)